Computer systems and related technologies such as the Internet have transformed modern society. This has become even more apparent as digital image/video technologies have become dominant—in business, on the Internet and in the home. In many homes for example, digital technologies such as DVD and digital cameras have replaced older analog technologies. As the Internet has exploded in popularity, digital images and moving pictures are routinely transmitted countless times per day. As these technologies have increased in popularity however, technical challenges relating thereto still remain. Stored digital images often times require storing/transmitting large amounts of data in order to reproduce a desired image or in the case of video, a desired sequence of images. In the case of the Internet for example, transmitting large amounts of data reduces network efficiency/speed and increases user frustration in relation to delays waiting for requested data. Thus, systems designers and architects have developed digital compression systems to reduce data storage and transmission requirements associated with digitized images.
Many conventional compression systems, however, provide the quantized coefficients to the encoder by scanning the coefficients in predictable, if not well-known, patterns (e.g., repeated horizontal scans starting from the same side of a plurality of coefficients stored in groups). Unfortunately, these types of scanning patterns may not enable efficient compression within the encoder since the scanning pattern may affect correlation between coefficient groups, and the efficiency of the encoder (e.g., longer vs. shorter runs of encoding sequences).
In relation to compression of images, redundant image features (e.g. long runs of a similar color) are often exploited to enable reduction of stored data. A common characteristic of many images is that neighboring pixels are correlated and therefore contain redundant information. Thus, an image compression objective is to determine a less correlated representation of the image. As an example, this objective may be achieved via redundancy and irrelevancy reduction. Redundancy reduction is directed at removing duplication from a signal source such as image and/or video, whereas irrelevancy reduction omits/filters parts of the image that may not be noticed and/or perceived by humans. In general, three types of redundancy may be identified: Spatial Redundancy or correlation between neighboring pixel values, Spectral Redundancy or correlation between different color planes or spectral bands, and Temporal Redundancy or correlation between adjacent frames in a sequence of images (e.g., video applications).
Image compression generally attempts to reduce the number of bits needed to represent an image by removing the spatial and spectral redundancies as much as possible. One popular image compression technology has been the Joint Photographic Experts Group (JPEG) standard. While JPEG is still employed in many applications, performance of coders based on this standard generally degrade at low bit rates mainly due to an underlying block-based Discrete Cosine Transform (DCT) scheme. More recently however, wavelet transform based coding has emerged in the field of image compression. According to wavelet-based technologies, image pixels are linearly transformed into a domain of wavelet coefficients via a discrete wavelet transform, for example. The wavelet coefficients may then be quantized wherein the number of bits required to store the transformed coefficients are reduced by reducing the precision of the coefficients, thus providing compression of the transformed data. The quantized data may then be scanned by an encoder (e.g., run-length encoder), wherein further compression may be achieved.